Multilevel logistic regression models were constructed to predict the 5-year mortality of Scots pine (Pinus sylvestris L.) and pubescent birch (Betula pubescens Ehrh.) growing in drained peatland stands in northern and central Finland. Data concerning tree mortality were obtained from two successive measurements of the National Forest Inventory-based permanent sample plot data base covering pure and mixed stands of Scots pine and pubescent birch. In the modeling data, Scots pine showed an average observed mortality of 2.73% compared to 2.98% for pubescent birch. In the model construction, stepwise logistic regression and multilevel models methods were applied, the latter making it possible to address the hierarchical data, thus obtaining unbiased estimates for model parameters. For both species, mortality was explained by tree size, competitive position, stand density, species admixture, and site quality. The expected need for ditch network maintenance or re-paludifi cation did not infl uence mortality. The multilevel models showed the lowest bias in the modeling data. The models were further validated against independent test data and by embedding them in a stand simulator. In 100-year simulations with different initial stand conditions, the models resulted in a 72% and 66% higher total mortality rate for the stem numbers of pine and birch, respectively, compared to previously used mortality models. The developed models are expected to improve the accuracy of stand forecasts in drained peatland sites.
The boreal forest will be strongly affected by climate change and in turn, these vast ecosystems may significantly impact global climatology and hydrology due to their exchanges of carbon and water with the atmosphere. It is now crucial to understand the intricate relationships between precipitation and evapotranspiration in these environments, particularly in less-studied locations characterized by a cold and humid climate. This study presents state-of-the-art measurements of energy and water budgets components over three years (2016)(2017)(2018) at the Montmorency Forest, Québec, Canada: a balsam fir boreal forest that receives ~1600 mm of precipitation annually (continental subarctic climate; Köppen classification subtype Dfc). Precipitation, evapotranspiration and potential evapotranspiration at the site are compared with observations from thirteen experimental sites around the world. These intercomparison sites (89 study-years) encompass various types of climate and vegetation (black spruces, jack pines, etc.) encountered in boreal forests worldwide. The Montmorency Forest stands out by receiving the largest amount of precipitation. Across all sites, water availability seems to be the principal evapotranspiration constraint, as precipitation tends to be more influential than potential evapotranspiration and other factors. This leads to the Montmorency Forest generating the largest amount of evapotranspiration, on average ~550 mm y −1 . This value appears to be an ecosystem maximum for evapotranspiration, which may be explained either by a physiological limit or a limited energy availability due to the presence of cloud cover. The Montmorency Forest water budget evacuates the precipitation excess mostly by watershed discharges, at an average rate of ~1050 mm y −1 , with peaks during the spring freshet. This behaviour, typical of mountainous headwater basins, necessarily influence downstream hydrological regimes to a large extent. This study provides a much needed insight in the hydrological regimes of a humid boreal-forested mountainous watershed, a type of basin rarely studied with precise energy and water budgets before.
In snow-fed catchments, it is crucial to monitor and model the snow water equivalent (SWE), particularly when simulating the melt water runoff. SWE distribution can, however, be highly heterogeneous, particularly in forested environments. Within these locations, scant studies have explored the spatiotemporal variability in SWE in relation with vegetation characteristics, with only few successful attempts. The aim of this paper is to fill this knowledge gap, through a detailed monitoring at nine locations within a 3.49 km 2 forested catchment in southern Québec, Canada (47 N, 71 W). The catchment receives an annual average of 633 mm of solid precipitation and is predominantly covered with balsam fir stands. Extracted from intensive field campaign and high-resolution LiDAR data, this study explores the effect of fine scale forest features (tree height, tree diameter, canopy density, leaf area index [LAI], tree density and gap fraction) on the spatiotemporal variability in the SWE distribution. A nested stratified random sampling design was adopted to quantify small-scale variability across the catchment and 1810 manual snow samples were collected throughout the consecutive winters of 2016-17 and 2017-18. This study explored the variability of SWE using coefficients of variation (CV) and relating to the LAI. We also present existing spatiotemporal differences in maximum snow depth across different stands and its relationship with average tree diameter. Furthermore, exploiting key vegetation characteristics, this paper explores different approaches to model SWE, such as multiple linear regression, binary regression tree and neural networks (NN).We were unable to establish any relationship between the CV of SWE and the LAI.However, we observed an increase in maximum snow depth with decreasing tree diameter, suggesting an association between these variables. NN modelling (Nash-Sutcliffe efficiency [NSE] = 0.71) revealed that, snow depth, snowpack age and forest characteristics (tree diameter and tree density) are key controlling variables on SWE.Using only variables that are deemed to be more readily available (snow depth, tree height, snowpack age and elevation), NN performance falls by only 7% (NSE = 0.66).
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